Meta's New Incremental Attribution Model: Truth, Hype, or Another Layer of Opacity?
Ad agencies are buzzing about Meta’s new Incremental Attribution model with some calling it a breakthrough in “real” measurement and the end of vanity metrics.
But here’s what we’re not seeing: Critical discussion from a data perspective.
When Optimization Becomes Manipulation
A friend once remarked to me that "unintentional evil is still evil," referencing how social media platforms might not have been designed with malicious intent, yet their engagement-obsessed algorithms often create harmful outcomes. Sometimes it's not evil but simply unethical, disrespecting humans, breaking trust, or manipulating behavior.
Why We Created 'A is for Analytics'
From an early age, children are naturally curious, asking questions, observing patterns, and trying to make sense of the world around them. In our modern, data-driven society, this innate curiosity can be nurtured through tools that teach them how to think critically, analyze information, and draw meaningful conclusions. This is why we created A is for Analytics—to make the world of data accessible to kids in a fun and engaging way.
As Analysts, We Must Approach Data with Skepticism and Critical Thinking
Critical thinking and skepticism are not just optional tools for data analysts—they are essential. Without them, we risk allowing flawed, biased, or incomplete data to influence important decisions. In a world where data is used to shape narratives and drive opinions, our responsibility is to approach it with a healthy dose of doubt and a commitment to uncovering the full story.
Creating a New Data Literate Generation
Through almost 20 years of experience, one of the biggest problems we’ve observed, not just within companies but within schools, communities, and the world in general, is a frightening lack of data literacy. Not only has this lack of data literacy translated into missed business opportunities to create really positive experiences for customers, but in a more important and serious way, the lack of data literacy has allowed companies, journalists, and politicians to effectively distort data in order to create divisiveness and chaos.